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| Titel |
Estimation of water quality parameters applying satellite data fusion and mining techniques in the lake Albufera de Valencia (Spain) |
| VerfasserIn |
Carolina Doña, Ni-Bin Chang, Benjamin W. Vannah, Juan Manuel Sánchez, Jesus Delegido, Antonio Camacho, Vicente Caselles |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250093203
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| Publikation (Nr.) |
 EGU/EGU2014-7723.pdf |
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| Zusammenfassung |
| Linked to the enforcement of the European Water Framework Directive (2000)
(WFD), which establishes that all countries of the European Union have to avoid
deterioration, improve and retrieve the status of the water bodies, and maintain their good
ecological status, several remote sensing studies have been carried out to monitor and
understand the water quality variables trend. Lake Albufera de Valencia (Spain) is
a hypereutrophic system that can present chrorophyll a concentrations over 200
mg-
m-3 and transparency (Secchi disk) values below 20 cm, needing to retrieve and
improve its water quality. The principal aim of our work was to develop algorithms to
estimate water quality parameters such as chlorophyll a concentration and water
transparency, which are informative of the eutrophication and ecological status, using
remote sensing data. Remote sensing data from Terra/MODIS, Landsat 5-TM and
Landsat 7-ETM+ images were used to carry out this study. Landsat images are
useful to analyze the spatial variability of the water quality variables, as well as to
monitor small to medium size water bodies due to its 30-m spatial resolution. But,
the poor temporal resolution of Landsat, with a 16-day revisit time, is an issue. In
this work we tried to solve this data gap by applying fusion techniques between
Landsat and MODIS images. Although the lower spatial resolution of MODIS is
250/500-m, one image per day is available. Thus, synthetic Landsat images were
created using data fusion for no data acquisition dates. Good correlation values
were obtained when comparing original and synthetic Landsat images. Genetic
programming was used to develop models for predicting water quality. Using the
reflectance bands of the synthetic Landsat images as inputs to the model, values of
R2 = 0.94 and RMSE = 8 mg-
m-3 were obtained when comparing modeled and
observed values of chlorophyll a, and values of R2= 0.91 and RMSE = 4 cm for the
transparency (Secchi disk). Finally, concentration maps estimating distributions of
chlorophyll a and transparency were obtained by applying these algorithms to the entire
synthetic images. These results show the technique exposed as an attractive tool to
monitor and study the spatio-temporal trend of these water quality parameters. |
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